Recent work further suggests that value prioritization is not fixed but context-sensitive. Murthy et al. [37] find that assistant-style models tend by default to privilege informational utility (helpfulness) over social utility (harmlessness), yet explicit in-context reinforcement of an alternative value can reliably shift output preferences. From a theoretical perspective, the Off-Switch Game [28] formalizes the importance of value uncertainty: systems that act with excessive confidence in a single objective may resist correction, whereas calibrated uncertainty about human preferences functions as a safety mechanism. However, personalization in LLMs introduces additional alignment challenges, as tailoring behavior to individual users can degrade safety performance [29] and increase the likelihood that agent–human interactions elicit unsafe behaviors.
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